{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_corr_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Fitting and training\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.50      0.33      0.40         9\n",
      "         Buy       0.20      0.40      0.27         5\n",
      "        Hold       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.29        17\n",
      "   macro avg       0.23      0.24      0.22        17\n",
      "weighted avg       0.32      0.29      0.29        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'n_estimators': [10,50,100,200],\n",
    "          'criterion': ['gini', 'entropy'],\n",
    "          'max_depth': [None, 2, 5, 10],\n",
    "          'min_samples_split': [5,10],\n",
    "          'min_samples_leaf': [1, 2, 5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 192 candidates, totalling 576 fits\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10, score=(train=0.827, test=0.223), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10, score=(train=0.982, test=0.194), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10, score=(train=0.965, test=0.473), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.306), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.361), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.205), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.284), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=0.966, test=0.242), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.214), total=   0.3s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=0.961, test=0.237), total=   0.3s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=0.966, test=0.441), total=   0.3s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200, score=(train=0.702, test=0.212), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200, score=(train=0.684, test=0.243), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200, score=(train=0.867, test=0.256), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=10, score=(train=0.566, test=0.212), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=10, score=(train=0.493, test=0.222), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=10, score=(train=0.732, test=0.205), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.567, test=0.278), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.650, test=0.260), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.734, test=0.234), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.566, test=0.262), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.560, test=0.256), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.732, test=0.299), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=0.567, test=0.212), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=0.684, test=0.217), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=0.748, test=0.208), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=10 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=10, score=(train=0.534, test=0.190), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=10 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=10, score=(train=0.561, test=0.249), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=10 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=10, score=(train=0.603, test=0.137), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50, score=(train=0.652, test=0.262), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50, score=(train=0.739, test=0.256), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50, score=(train=0.531, test=0.208), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100, score=(train=0.514, test=0.262), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100, score=(train=0.684, test=0.243), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100, score=(train=0.639, test=0.133), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200, score=(train=0.601, test=0.262), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200, score=(train=0.667, test=0.190), total=   0.2s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200, score=(train=0.732, test=0.208), total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done 576 out of 576 | elapsed:  1.8min finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=10, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 2, 5, 10],\n",
       "                         'min_samples_leaf': [1, 2, 5],\n",
       "                         'min_samples_split': [5, 10],\n",
       "                         'n_estimators': [10, 50, 100, 200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.7203914228564962\n",
      "Mean Testing Score: 1.0\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 50}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=5,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=50,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.62      0.56      0.59         9\n",
      "         Buy       0.44      0.80      0.57         5\n",
      "        Hold       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.53        17\n",
      "   macro avg       0.36      0.45      0.39        17\n",
      "weighted avg       0.46      0.53      0.48        17\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
